The original GAN has been extended into various forms such as the Wasserstein GAN (WGAN), Convolutional-based GANs (DC-GANs), Semi-supervised GANs (SSGANs), and Condition-based GANs (CGAN) to enhance the quality of data synthesis and improve the training process. For instance, the complexities of controlling the adversarial process between generator and discriminator cause a mode collapse/gradient disappearance phenomenon leading to unsatisfactory data generation performance of the GAN models. To overcome this challenge, Arjovsky et al. [1]} introduced the Wasserstein GAN to deal with mode collapse phenomena. It provided a solution to the instability problem of GAN but had the challenge of weight clipping which was addressed by Gao et al. [2]} through the combination of WGAN with a gradient penalty. Zhang et al. [3]} also tried solving the small sample problem, focusing on intelligent fault diagnosis via multi-module gradient penalized GAN. The proposed method comprises three network modules: generator, discriminator, and classifier. The mechanical signals are generated by adversarial training and are then used as training data. [4]}, and [5]} also used DCGAN and ACGAN-SDAE, respectively, for the fault diagnosis problem of rotating machinery.
